# Introduction to R

## Overview

Teaching:50 min

Exercises:30 minQuestions

What data types are available in R?

What is an object?

How can values be initially assigned to variables of different data types?

What arithmetic and logical operators can be used?

How can subsets be extracted from vectors?

How does R treat missing values?

How can we deal with missing values in R?

Objectives

Define the following terms as they relate to R: object, assign, call, function, arguments, options.

Assign values to objects in R.

Learn how to name objects.

Use comments to inform script.

Solve simple arithmetic operations in R.

Call functions and use arguments to change their default options.

Inspect the content of vectors and manipulate their content.

Subset and extract values from vectors.

Analyze vectors with missing data.

## Creating objects in R

You can get output from R simply by typing math in the console:

```
3 + 5
```

```
[1] 8
```

```
12 / 7
```

```
[1] 1.714286
```

However, to do useful and interesting things, we need to assign *values* to
*objects*. To create an object, we need to give it a name followed by the
assignment operator `<-`

, and the value we want to give it:

```
area_hectares <- 1.0
```

`<-`

is the assignment operator. It assigns values on the right to objects on
the left. So, after executing `x <- 3`

, the value of `x`

is `3`

. The arrow can
be read as 3 **goes into** `x`

. For historical reasons, you can also use `=`

for assignments, but not in every context. Because of the
slight differences
in syntax, it is good practice to always use `<-`

for assignments. More
generally we prefer the `<-`

syntax over `=`

because it makes it clear what
direction the assignment is operating (left assignment), and it increases the
read-ability of the code.

In RStudio, typing `Alt` + `-` (push `Alt` at the
same time as the `-` key) will write `<- `

in a single keystroke in a
PC, while typing `Option` + `-` (push `Option` at the
same time as the `-` key) does the same in a Mac.

Objects can be given any name such as `x`

, `current_temperature`

, or
`subject_id`

. You want your object names to be explicit and not too long. They
cannot start with a number (`2x`

is not valid, but `x2`

is). R is case sensitive
(e.g., `age`

is different from `Age`

). There are some names that
cannot be used because they are the names of fundamental objects in R (e.g.,
`if`

, `else`

, `for`

, see
here
for a complete list). In general, even if it’s allowed, it’s best to not use
them (e.g., `c`

, `T`

, `mean`

, `data`

, `df`

, `weights`

). If in
doubt, check the help to see if the name is already in use. It’s also best to
avoid dots (`.`

) within an object name as in `my.dataset`

. There are many
objects in R with dots in their names for historical reasons, but because dots
have a special meaning in R (for methods) and other programming languages, it’s
best to avoid them. The recommended writing style it’s called snake_case, which
implies using only lowercaseletters and numbers and separating each word with
underscores (e.g., animals_weight, average_income). It is also recommended to use nouns for object names, and
verbs for function names. It’s important to be consistent in the styling of your
code (where you put spaces, how you name objects, etc.). Using a consistent
coding style makes your code clearer to read for your future self and your
collaborators. In R, three popular style guides are
Google’s, Jean
Fan’s and the
tidyverse’s. The tidyverse’s is very
comprehensive and may seem overwhelming at first. You can install the
** lintr** package to automatically check
for issues in the styling of your code.

## Objects vs. variables

What are known as

`objects`

in`R`

are known as`variables`

in many other programming languages. Depending on the context,`object`

and`variable`

can have drastically different meanings. However, in this lesson, the two words are used synonymously. For more information see: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects

When assigning a value to an object, R does not print anything. You can force R to print the value by using parentheses or by typing the object name:

```
area_hectares <- 1.0 # doesn't print anything
(area_hectares <- 1.0) # putting parenthesis around the call prints the value of `area_hectares`
```

```
[1] 1
```

```
area_hectares # and so does typing the name of the object
```

```
[1] 1
```

Now that R has `area_hectares`

in memory, we can do arithmetic with it. For
instance, we may want to convert this area into acres (area in acres is 2.47 times the area in hectares):

```
2.47 * area_hectares
```

```
[1] 2.47
```

We can also change an object’s value by assigning it a new one:

```
area_hectares <- 2.5
2.47 * area_hectares
```

```
[1] 6.175
```

This means that assigning a value to one object does not change the values of
other objects. For example, let’s store the plot’s area in acres
in a new object, `area_acres`

:

```
area_acres <- 2.47 * area_hectares
```

and then change `area_hectares`

to 50.

```
area_hectares <- 50
```

## Exercise

What do you think is the current content of the object

`area_acres`

? 123.5 or 6.175?## Solution

The value of

`area_acres`

is still 6.175 because you have not re-run the line`area_acres <- 2.47 * area_hectares`

since changing the value of`area_hectares`

.

## Comments

All programming languages allow the programmer to include comments in their code. Including comments to your code has many advantages: it helps you explain your reasoning and it forces you to be tidy. A commented code is also a great tool not only to your collaborators, but to your future self. Comments are the key to a reproducible analysis.

To do this in R we use the `#`

character.
Anything to the right of the `#`

sign and up to the end of the line is treated as a comment and is ignored by R. You can start lines with comments
or include them after any code on the line.

```
area_hectares <- 1.0 # land area in hectares
area_acres <- area_hectares * 2.47 # convert to acres
area_acres # print land area in acres.
```

```
[1] 2.47
```

RStudio makes it easy to comment or uncomment a paragraph: after selecting the
lines you want to comment, press at the same time on your keyboard
`Ctrl` + `Shift` + `C`. If you only want to comment
out one line, you can put the cursor at any location of that line (i.e. no need
to select the whole line), then press `Ctrl` + `Shift` +
`C`.

## Exercise

Create two variables

`r_length`

and`r_width`

and assign them values. It should be noted that, because`length`

is a built-in R function, R Studio might add “()” after you type`length`

and if you leave the parentheses you will get unexpected results. This is why you might see other programmers abbreviate common words. Create a third variable`r_area`

and give it a value based on the current values of`r_length`

and`r_width`

. Show that changing the values of either`r_length`

and`r_width`

does not affect the value of`r_area`

.## Solution

`r_length <- 2.5 r_width <- 3.2 r_area <- r_length * r_width r_area`

`[1] 8`

`# change the values of r_length and r_width r_length <- 7.0 r_width <- 6.5 # the value of r_area isn't changed r_area`

`[1] 8`

### Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands
including operations assignments, etc. Many functions are predefined, or can be
made available by importing R *packages* (more on that later). A function
usually gets one or more inputs called *arguments*. Functions often (but not
always) return a *value*. A typical example would be the function `sqrt()`

. The
input (the argument) must be a number, and the return value (in fact, the
output) is the square root of that number. Executing a function (‘running it’)
is called *calling* the function. An example of a function call is:

```
b <- sqrt(a)
```

Here, the value of `a`

is given to the `sqrt()`

function, the `sqrt()`

function
calculates the square root, and returns the value which is then assigned to
the object `b`

. This function is very simple, because it takes just one argument.

The return ‘value’ of a function need not be numerical (like that of `sqrt()`

),
and it also does not need to be a single item: it can be a set of things, or
even a dataset. We’ll see that when we read data files into R.

Arguments can be anything, not only numbers or filenames, but also other
objects. Exactly what each argument means differs per function, and must be
looked up in the documentation (see below). Some functions take arguments which
may either be specified by the user, or, if left out, take on a *default* value:
these are called *options*. Options are typically used to alter the way the
function operates, such as whether it ignores ‘bad values’, or what symbol to
use in a plot. However, if you want something specific, you can specify a value
of your choice which will be used instead of the default.

Let’s try a function that can take multiple arguments: `round()`

.

```
round(3.14159)
```

```
[1] 3
```

Here, we’ve called `round()`

with just one argument, `3.14159`

, and it has
returned the value `3`

. That’s because the default is to round to the nearest
whole number. If we want more digits we can see how to do that by getting
information about the `round`

function. We can use `args(round)`

or look at the
help for this function using `?round`

.

```
args(round)
```

```
function (x, digits = 0)
NULL
```

```
?round
```

We see that if we want a different number of digits, we can
type `digits=2`

or however many we want.

```
round(3.14159, digits = 2)
```

```
[1] 3.14
```

If you provide the arguments in the exact same order as they are defined you don’t have to name them:

```
round(3.14159, 2)
```

```
[1] 3.14
```

And if you do name the arguments, you can switch their order:

```
round(digits = 2, x = 3.14159)
```

```
[1] 3.14
```

It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to specify the names of all optional arguments. If you don’t, someone reading your code might have to look up the definition of a function with unfamiliar arguments to understand what you’re doing.

## Exercise

Type in

`?round`

at the console and then look at the output in the Help pane. What other functions exist that are similar to`round`

? How do you use the`digits`

parameter in the round function?

## Vectors and data types

A vector is the most common and basic data type in R, and is pretty much
the workhorse of R. A vector is composed by a series of values, which can be
either numbers or characters. We can assign a series of values to a vector using
the `c()`

function. For example we can create a vector of the number of household
members for the households we’ve interviewed and assign
it to a new object `hh_members`

:

```
hh_members <- c(3, 7, 10, 6)
hh_members
```

```
[1] 3 7 10 6
```

A vector can also contain characters. For example, we can have
a vector of the building material used to construct our
interview respondents’ walls (`respondent_wall_type`

):

```
respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type
```

```
[1] "muddaub" "burntbricks" "sunbricks"
```

The quotes around “muddaub”, etc. are essential here. Without the quotes R
will assume there are objects called `muddaub`

, `burntbricks`

and `sunbricks`

. As these objects
don’t exist in R’s memory, there will be an error message.

There are many functions that allow you to inspect the content of a
vector. `length()`

tells you how many elements are in a particular vector:

```
length(hh_members)
```

```
[1] 4
```

```
length(respondent_wall_type)
```

```
[1] 3
```

An important feature of a vector, is that all of the elements are the same type of data.
The function `typeof()`

indicates the type of an object:

```
typeof(hh_members)
```

```
[1] "double"
```

```
typeof(respondent_wall_type)
```

```
[1] "character"
```

The function `str()`

provides an overview of the structure of an object and its
elements. It is a useful function when working with large and complex
objects:

```
str(hh_members)
```

```
num [1:4] 3 7 10 6
```

```
str(respondent_wall_type)
```

```
chr [1:3] "muddaub" "burntbricks" "sunbricks"
```

You can use the `c()`

function to add other elements to your vector:

```
possessions <- c("bicycle", "radio", "television")
possessions <- c(possessions, "mobile_phone") # add to the end of the vector
possessions <- c("car", possessions) # add to the beginning of the vector
possessions
```

```
[1] "car" "bicycle" "radio" "television" "mobile_phone"
```

In the first line, we take the original vector `possessions`

,
add the value `"mobile_phone"`

to the end of it, and save the result back into
`possessions`

. Then we add the value `"car"`

to the beginning, again saving the result
back into `possessions`

.

We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.

An **atomic vector** is the simplest R **data type** and is a linear vector of a single type. Above, we saw
2 of the 6 main **atomic vector** types that R
uses: `"character"`

and `"numeric"`

(or `"double"`

). These are the basic building blocks that
all R objects are built from. The other 4 **atomic vector** types are:

`"logical"`

for`TRUE`

and`FALSE`

(the boolean data type)`"integer"`

for integer numbers (e.g.,`2L`

, the`L`

indicates to R that it’s an integer)`"complex"`

to represent complex numbers with real and imaginary parts (e.g.,`1 + 4i`

) and that’s all we’re going to say about them`"raw"`

for bitstreams that we won’t discuss further

You can check the type of your vector using the `typeof()`

function and inputting your vector as the argument.

Vectors are one of the many **data structures** that R uses. Other important
ones are lists (`list`

), matrices (`matrix`

), data frames (`data.frame`

),
factors (`factor`

) and arrays (`array`

).

## Exercise

We’ve seen that atomic vectors can be of type character, numeric (or double), integer, and logical. But what happens if we try to mix these types in a single vector?

## Solution

R implicitly converts them to all be the same type.

What will happen in each of these examples? (hint: use

`class()`

to check the data type of your objects):`num_char <- c(1, 2, 3, "a") num_logical <- c(1, 2, 3, TRUE) char_logical <- c("a", "b", "c", TRUE) tricky <- c(1, 2, 3, "4")`

Why do you think it happens?

## Solution

Vectors can be of only one data type. R tries to convert (coerce) the content of this vector to find a “common denominator” that doesn’t lose any information.

How many values in

`combined_logical`

are`"TRUE"`

(as a character) in the following example:`num_logical <- c(1, 2, 3, TRUE) char_logical <- c("a", "b", "c", TRUE) combined_logical <- c(num_logical, char_logical)`

## Solution

Only one. There is no memory of past data types, and the coercion happens the first time the vector is evaluated. Therefore, the

`TRUE`

in`num_logical`

gets converted into a`1`

before it gets converted into`"1"`

in`combined_logical`

.You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class

coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

## Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

```
respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type[2]
```

```
[1] "burntbricks"
```

```
respondent_wall_type[c(3, 2)]
```

```
[1] "sunbricks" "burntbricks"
```

We can also repeat the indices to create an object with more elements than the original one:

```
more_respondent_wall_type <- respondent_wall_type[c(1, 2, 3, 2, 1, 3)]
more_respondent_wall_type
```

```
[1] "muddaub" "burntbricks" "sunbricks" "burntbricks" "muddaub"
[6] "sunbricks"
```

R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

### Conditional subsetting

Another common way of subsetting is by using a logical vector. `TRUE`

will
select the element with the same index, while `FALSE`

will not:

```
hh_members <- c(3, 7, 10, 6)
hh_members[c(TRUE, FALSE, TRUE, TRUE)]
```

```
[1] 3 10 6
```

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. For instance, if you wanted to select only the values above 5:

```
hh_members > 5 # will return logicals with TRUE for the indices that meet the condition
```

```
[1] FALSE TRUE TRUE TRUE
```

```
## so we can use this to select only the values above 5
hh_members[hh_members > 5]
```

```
[1] 7 10 6
```

You can combine multiple tests using `&`

(both conditions are true, AND) or `|`

(at least one of the conditions is true, OR):

```
hh_members[hh_members < 4 | hh_members > 7]
```

```
[1] 3 10
```

```
hh_members[hh_members >= 4 & hh_members <= 7]
```

```
[1] 7 6
```

Here, `<`

stands for “less than”, `>`

for “greater than”, `>=`

for “greater than
or equal to”, and `==`

for “equal to”. The double equal sign `==`

is a test for
numerical equality between the left and right hand sides, and should not be
confused with the single `=`

sign, which performs variable assignment (similar
to `<-`

).

A common task is to search for certain strings in a vector. One could use the
“or” operator `|`

to test for equality to multiple values, but this can quickly
become tedious.

```
possessions <- c("car", "bicycle", "radio", "television", "mobile_phone")
possessions[possessions == "car" | possessions == "bicycle"] # returns both car and bicycle
```

```
[1] "car" "bicycle"
```

The function `%in%`

allows you to test if any of the elements of a search vector
(on the left hand side) are found in the target vector (on the right hand side):

```
possessions %in% c("car", "bicycle")
```

```
[1] TRUE TRUE FALSE FALSE FALSE
```

Note that the output is the same length as the search vector on the left hand
side, because `%in%`

checks whether each element of the search vector is found
somewhere in the target vector. Thus, you can use `%in%`

to select the elements
in the search vector that appear in your target vector:

```
possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")
```

```
[1] TRUE TRUE FALSE FALSE FALSE
```

```
possessions[possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")]
```

```
[1] "car" "bicycle"
```

## Missing data

As R was designed to analyze datasets, it includes the concept of missing data
(which is uncommon in other programming languages). Missing data are represented
in vectors as `NA`

.

When doing operations on numbers, most functions will return `NA`

if the data
you are working with include missing values. This feature
makes it harder to overlook the cases where you are dealing with missing data.
You can add the argument `na.rm=TRUE`

to calculate the result while ignoring
the missing values.

```
rooms <- c(2, 1, 1, NA, 7)
mean(rooms)
```

```
[1] NA
```

```
max(rooms)
```

```
[1] NA
```

```
mean(rooms, na.rm = TRUE)
```

```
[1] 2.75
```

```
max(rooms, na.rm = TRUE)
```

```
[1] 7
```

If your data include missing values, you may want to become familiar with the
functions `is.na()`

, `na.omit()`

, and `complete.cases()`

. See below for
examples.

```
## Extract those elements which are not missing values.
## The ! character is also called the NOT operator
rooms[!is.na(rooms)]
```

```
[1] 2 1 1 7
```

```
## Count the number of missing values.
## The output of is.na() is a logical vector (TRUE/FALSE equivalent to 1/0) so the sum() function here is effectively counting
sum(is.na(rooms))
```

```
[1] 1
```

```
## Returns the object with incomplete cases removed. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
na.omit(rooms)
```

```
[1] 2 1 1 7
attr(,"na.action")
[1] 4
attr(,"class")
[1] "omit"
```

```
## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
rooms[complete.cases(rooms)]
```

```
[1] 2 1 1 7
```

Recall that you can use the `typeof()`

function to find the type of your atomic vector.

## Exercise

Using this vector of rooms, create a new vector with the NAs removed.

`rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)`

Use the function

`median()`

to calculate the median of the`rooms`

vector.Use R to figure out how many households in the set use more than 2 rooms for sleeping.

## Solution

`rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1) rooms_no_na <- rooms[!is.na(rooms)] # or rooms_no_na <- na.omit(rooms) # 2. median(rooms, na.rm = TRUE)`

`[1] 1`

`# 3. rooms_above_2 <- rooms_no_na[rooms_no_na > 2] length(rooms_above_2)`

`[1] 4`

Now that we have learned how to write scripts, and the basics of R’s data structures, we are ready to start working with the SAFI dataset we have been using in the other lessons, and learn about data frames.

## Key Points

Access individual values by location using

`[]`

.Access arbitrary sets of data using

`[c(...)]`

.Use logical operations and logical vectors to access subsets of data.